The traditional approach of credit institutions to assessing borrowers involves examining their credit history. However, traditional data alone may not provide a complete picture of a borrower's financial capacity.
I'll explain with an example why this is the case.
According to TransUnion, millions of consumers have never had any credit products. For instance, in the United States, this figure is 8.1 million people; in India, it's 571 million, accounting for 63% of the country's adult population.
This means that the chances of these millions of consumers getting a loan are quite low. Traditional lending organizations often avoid lending money to clients without a credit history. It's because they cannot assess their creditworthiness.
That's why I want to discuss lenders using alternative data analysis to assess credit risks and the opportunities they create for lending institutions.
Recent technological advancements have made alternative data more popular in credit scoring. These include AI and its ML applications, GenAI, the Internet of Things, predictive analytics, etc.
According to Research and Markets, the size of the alternative data market is expected to reach $156.23 billion by 2030.
The industry's rapid growth is driven by modern credit organizations' inability to use alternative data in business.
Relying only on credit scores leads to missed clients, reduced competitiveness, and income loss. Institutions need to reconsider their decision-making.
Next, I suggest a detailed discussion on how alternative data analysis helps companies involved in lending.
Lending institutions can approve more loans. They need to use more than traditional data sources. They can extend credit to potential clients with low ratings due to a lack of credit history.
Lenders can understand a borrower's finances better by checking extra details. Even if someone repays loans on time, they might still have late payments for utilities or rent.
Modern fintech tools also use AI-powered predictive analytics. They process vast amounts of data, track trends and patterns, and make informed decisions about granting loans.
Using alternative data is crucial in current lending trends. It provides extra information about the borrower and helps decide on loan applications.
Lending companies can use alternative data sources to check client identities. Also, AI and ML software can find suspicious behavior linked to fraud.
Extra information about potential clients is crucial for lending organizations' success.
Next, let's define what we mean by alternative data and discuss their examples below.
Modern lending institutions use alternative data to evaluate borrowers' creditworthiness.
For example, regular prepaid mobile payments may show responsibility and a stable income. Also, active hours from 9:00 to 18:00 suggest steady employment.
In New York, banks do a U.C.C. Search through the government portal ny.gov. It provides detailed information about debtors.
Alternative data can strengthen your position in the fast-changing financial industry. It offers several advantages.
Technologies based on AI, including predictive analytics, rely on the data they use. The more variables considered by predictive models, the more accurate the analysis results will be.
This helps predict financial indicators for borrowers, consumer demand, and market trends.
Two factors contribute to this:
Many popular software products can make a credit decision in just a few seconds.
Automated credit scoring models using alternative data are based on ML. This reduces the number of errors humans make in the underwriting process. It also reduces the time and cost of loan issuance.
Trying alternative data providers can be cheaper than traditional ones. This advantage is especially relevant for small companies with limited resources.
The more someone is active online, the better their chances of getting credit, even without a long credit history.
But, it only works if the lender uses alternative data. These include social networks, online transactions, and utility and rental payment details.
Enriching traditional financial risk assessment with alternative data can benefit credit organizations and their clients. Therefore, I recommend following the latest trends in the financial sector. Positive outcomes will follow.
RiskSeal offers the benefits of automated credit scoring through alternative data.
With RiskSeal, lending organizations can:
RiskSeal is based on ML technology, minimizing errors and providing a rapid response to loan applications.
Yes, RiskSeal offers such solutions. We created a modern credit scoring model for online lenders. It uses machine learning to process thousands of data points.
Lenders use alternative data to better understand borrowers, lower loan costs, provide favorable interest rates, and boost competitiveness.
Alternative data sources allow lenders to extend credit to customers without a credit history. Credit organizations often avoid lending to consumers with low credit ratings in traditional risk assessments.
Using alternative data analysis, lenders can assess a borrower's creditworthiness and identify potential defaulters early on.
Consumers can increase their chances of obtaining credit, even if they have never used such services. They can also expect faster decision-making on their applications.
Turning to alternative data providers can be more cost-effective than traditional financial information. This is particularly relevant for small lending organizations.